A Functional Central Limit Theorem for Locally Stationary Time Series in Banach Spaces

Abstract

We establish a functional central limit theorem for locally stationary time series in a separable Banach space $B$. The result requires no type-2 or cotype assumptions and thus covers spaces central to functional data analysis, such as $C([0,1])$ and $L^p([0,1])$. Under moment conditions, summable physical dependence coefficients and a bracketing entropy condition controlling the infinite-dimensional tails, the centered partial sum process converges weakly in $D([0,1],B)$ to a centered Gaussian process, whose covariance is the integrated local long-run covariance, an element of the projective tensor product. We also obtain a stochastic integral representation with respect to a cylindrical Brownian motion. As the limit depends on the unknown long-run covariance, we prove consistency of a block multiplier bootstrap for the partial sum process. This yields CUSUM tests for a constant mean of the full process, based on arbitrary continuous seminorms and valid under a time-varying long-run covariance. For linear projections, we derive a self-normalized test with a pivotal limit. In simulations, the supremum norm test detects localized changes that its $L^2$ counterpart largely misses, and applications to EEG and temperature data illustrate the methodology. Examples in $C([0,1])$ and $W_0^{1,p}([0,1])$ show how the entropy condition can be verified.

arXiv commentKeywords: Locally stationary time series; Banach-valued time series; functional central limit theorem; functional data analysis; block multiplier bootstrap; change point detection